Some participants argued that moredata is necessary to capture the full range of cultural expressions, while others contended thatthe focus should be on developing thicker development pipelines that incorporate expertiseand context. They discussed the limitations of current models, which often operate on crudemetrics and may not adequately represent the richness of cultural data.

They highlighted the importance of interdisciplinaryteaming at specific points in the development pipeline, imagining pairs of experts fromtechnical and qualitative fields working together step-by-step to negotiate approaches thatmeet shared goals. This collaborative approach would ensure that both qualitative insightsand quantitative rigor are incorporated into AI development.

common basic language for evaluating iterations of AI that do not assumethere is a linear or universal path of improving AI for all users, regardless of context.This shared language would help clarify where disciplinary specificity is needed and whereinterdisciplinary collaboration can be most effective.

how small language models can contribute to responsible innovation, and howto design decentralized infrastructure architectures that enable users to choose how theyshare and distribute their data and models

the need for alternativeapproaches to AI development that prioritize sustainability, justice, and inclusivity

The role of metadata is complex

ocused on the gaps between community knowledge and com-putational knowledge

Institutional aspects and interdisciplinarity play a significant role in the cultures of AIproduction. There is a need for alternative imaginaries of technology that go beyond thecorporate inclusion of data.

first exercise of the day was a series of “speed dating” rounds.